Investigating the application of artificial intelligence for earthquake prediction in Terengganu

Suzlyana Marhain (), Ali Najah Ahmed (), Muhammad Ary Murti (), Pavitra Kumar () and Ahmed El-Shafie ()
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Suzlyana Marhain: Universiti Tenaga Nasional (UNITEN)
Ali Najah Ahmed: Universiti Tenaga Nasional (UNITEN)
Muhammad Ary Murti: Telkom University
Pavitra Kumar: University of Malaya
Ahmed El-Shafie: University of Malaya

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 1, No 42, 977-999

Abstract: Abstract Earthquake is one of the devastating and frightening natural disasters that caused big casualties in a small duration. Earthquake caused lots of damage in just a few minutes and the casualties of the earthquake increase as the population increase which also contribute to higher amount of property and buildings. Therefore, by developing model capable of detecting the recurrence behaviour of earthquake helps in predicting earthquake as well as minimizing the casualties caused by the earthquake. In this report, a few of artificial intelligence algorithms such as support vector machine, boosted decision tree regression, random forest and multivariate adaptive regression spline will be used in the development of best model algorithm in earthquake prediction. Meteorological data are collected from several stations in Terengganu and processed for normalization and the data will be analysed using algorithms and its performance will be evaluated. Terengganu is situated on the east coast of Peninsular Malaysia and is bordered on the north-west and south-west by Kelantan and Pahang. Terengganu's east side is bordered by the South China Sea. Terengganu is located within the vicinity of the South China Sea, which is possible to be affected by the Marina Trench Earthquake. The subduction zone of Manila Trench is capable of producing a high magnitude of earthquake activity that can create a deadliest tsunami disaster. Therefore, Terengganu is studied for the investigation of artificial intelligence in earthquake prediction. The model algorithms are then analysed to measure its sensitivity and accuracy in prediction and consistency of the result.

Keywords: Earthquake prediction; Artificial intelligence; Machine learning algorithm; Support vector machine; Boosted decision tree regression; Random forest; Multivariate adaptive regression spline; Pearson correlation coefficient; Uncertainty analysis (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04716-7

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